| 000 -LEADER |
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| fixed length control field |
03442cam a2200301 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
jomaaum |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20220106143758.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION |
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| fixed length control field |
m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr cn |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
281020s2020 xx o eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781839214189 |
| 024 8# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
9781839214189 |
| 041 0# - Language |
|---|
| Language code of text/sound track or separate title |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
23 |
| Classification number |
006.312 |
| Item number |
C928 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Crickard, Paul, |
| Relator term |
author |
| 9 (RLIN) |
44899 |
| 245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Title |
Data Engineering with Python / |
| Statement of responsibility, etc |
Crickard, Paul |
| 260 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Packt Publishing, |
| Name of publisher, distributor, etc |
Birmingham: |
| Date of publication, distribution, etc |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii , 356 p. ; |
| Dimensions |
24 cm. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Available to OhioLINK libraries |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book Description Data engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You'll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You'll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you'll build architectures on which you'll learn how to deploy data pipelines. By the end of this Python book, you'll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production. What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT prof.. |
| 533 ## - REPRODUCTION NOTE |
|---|
| Type of reproduction |
Electronic reproduction. |
| Place of reproduction |
Boston, MA : |
| Agency responsible for reproduction |
Safari, |
| Note about reproduction |
Available via World Wide Web. |
| Date of reproduction |
2020 |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web |
| 550 ## - ISSUING BODY NOTE |
|---|
| Issuing body note |
Made available through: Safari, an O'Reilly Media Company |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence. |
|
|---|
| Topical term or geographic name as entry element |
Engineering. |
|
|---|
| Topical term or geographic name as entry element |
Software engineering. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Book |
